Implementasi Jaringan Syaraf Tiruan Recurrent menggunakan Gradient Descent Adaptive Learning Rate and Momentum untuk Pendugaan Curah Hujan
Journal: Comtech (Vol.2, No. 1)Publication Date: 2011-06-29
Authors : Afan Galih Salman; Yen Lina Prasetio;
Page : 23-35
Keywords : artificial neural network; coefficient deteminationi (R2); root mean square error (RMSE); gradient descent adaptive learning rate and momentum; ENSO.;
Abstract
The artificial neural network (ANN) technology in rainfall prediction can be done using the learning approach. The ANN prediction accuracy is measured by the determination coefficient (R2) and root mean square error (RMSE). This research implements Elmans Recurrent ANN which is heuristically optimized based on el-nino southern oscillation(ENSO) variables: wind, southern oscillation index (SOI), sea surface temperature (SST) dan outgoing long wave radiation (OLR) to forecast regional monthly rainfall in Bongan Bali. The heuristic learning optimization done is basically a performance development of standard gradient descent learning algorithm into training algorithms: gradient descent momentum and adaptive learning rate. The patterns of input data affect the performance of Recurrent Elman neural network in estimation process. The first data group that is 75% training data and 25% testing data produce the maximum R2 leap 74,6% while the second data group that is 50% training data and 50% testing data produce the maximum R2 leap 49,8%.
Other Latest Articles
- PEMANFAATAN FITUR REMOTE CONTROL MELALUI MEDIA BLUETOOTH PADA TELEPON SELULER SEBAGAI ALAT BANTU PROSES PENGAJARAN
- SISTEM NILAI DENGAN BOBOT KOMPONEN YANG DINAMIS STUDI KASUS BINUS INTERNASIONAL
- PENGEMBANGAN SISTEM INFORMASI AKUNTANSI PT. SURYA WARFA KHARISMA STUDI KASUS: PENJULAN, PIUTANG, DAN PENERIMAAN KAS
- APLIKASI PIRANTI LUNAK PENGANALISIS SINTAKSIS KALIMAT BAHASA INDONESIA
- PENERAPAN REGULASI KESEHATAN RUMAH DAN KENDALANYA PADA PEMBANGUNAN RUMAH SEDERHANA SEHAT
Last modified: 2015-11-17 15:50:26